Strict SLAs for Operational Business Intelligence

Today, SLAs for SaaS business applications usually lack stringent service level objectives and significant penalties. Moreover, Operational Business Intelligence features of modern business applications, like analytic dashboards, result in mixed workloads which make it even more difficult to predict execution times accurately due to resource contention. In contrast to the traditional three-tier architecture, an architecture for SaaS business applications should combine application and database layer to allow for processing business transactions and queries according to a queuing approach which enables strict SLAs with stringent response time and throughput guarantees. With stricter SLAs it would be easier to compare different cloud offerings with on-premise solutions and thus cloud computing could become more attractive for potential customers.

[1]  Bo Gao,et al.  An Effective Heuristic for On-line Tenant Placement Problem in SaaS , 2010, 2010 IEEE International Conference on Web Services.

[2]  Fabio Panzieri,et al.  QoS–Aware Clouds , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[3]  Daniel J. Abadi,et al.  The case for determinism in database systems , 2010, Proc. VLDB Endow..

[4]  Michael Stonebraker,et al.  H-store: a high-performance, distributed main memory transaction processing system , 2008, Proc. VLDB Endow..

[5]  Alfons Kemper,et al.  HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[6]  Michael Stonebraker,et al.  The End of an Architectural Era (It's Time for a Complete Rewrite) , 2007, VLDB.

[7]  Christoph Koch,et al.  DBToaster: A SQL Compiler for High-Performance Delta Processing in Main-Memory Databases , 2009, Proc. VLDB Endow..

[8]  Alfons Kemper,et al.  A comparison of flexible schemas for software as a service , 2009, SIGMOD Conference.

[9]  Alfons Kemper,et al.  Quality of Service Enabled Database Applications , 2006, ICSOC.

[10]  Ramez Elmasri,et al.  Fundamentals of Database Systems, 5th Edition , 2006 .

[11]  Ramez Elmasri,et al.  Fundamentals of Database Systems , 1989 .

[12]  Times-Ten Team,et al.  In-memory data management for consumer transactions the timesten approach , 1999, SIGMOD '99.

[13]  Frank Leymann,et al.  A Framework for Optimized Distribution of Tenants in Cloud Applications , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[14]  Olivera Marjanovic The Next Stage of Operational Business Intelligence: Creating New Challenges for Business Process Management , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[15]  Alfons Kemper,et al.  Benchmarking Hybrid OLTP&OLAP Database Systems , 2011, BTW.

[16]  Martin L. Kersten,et al.  Database Architecture Evolution: Mammals Flourished long before Dinosaurs became Extinct , 2009, Proc. VLDB Endow..

[17]  Parag Agrawal,et al.  The case for RAMClouds: scalable high-performance storage entirely in DRAM , 2010, OPSR.

[18]  Alexander Zeier,et al.  A case for online mixed workload processing , 2010, DBTest '10.

[19]  Alfons Kemper,et al.  Model-Based Planning for State-Related Changes to Infrastructure and Software as a Service Instances in Large Data Centers , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[20]  Fabio Panzieri,et al.  QoSAware Clouds , 2010 .

[21]  Michael J. Cahill Serializable isolation for snapshot databases , 2009, TODS.

[22]  Fangzhe Chang,et al.  Optimal Resource Allocation in Clouds , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.